3,708 research outputs found
Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction
Tracking congestion throughout the network road is a critical component of
Intelligent transportation network management systems. Understanding how the
traffic flows and short-term prediction of congestion occurrence due to
rush-hour or incidents can be beneficial to such systems to effectively manage
and direct the traffic to the most appropriate detours. Many of the current
traffic flow prediction systems are designed by utilizing a central processing
component where the prediction is carried out through aggregation of the
information gathered from all measuring stations. However, centralized systems
are not scalable and fail provide real-time feedback to the system whereas in a
decentralized scheme, each node is responsible to predict its own short-term
congestion based on the local current measurements in neighboring nodes.
We propose a decentralized deep learning-based method where each node
accurately predicts its own congestion state in real-time based on the
congestion state of the neighboring stations. Moreover, historical data from
the deployment site is not required, which makes the proposed method more
suitable for newly installed stations. In order to achieve higher performance,
we introduce a regularized Euclidean loss function that favors high congestion
samples over low congestion samples to avoid the impact of the unbalanced
training dataset. A novel dataset for this purpose is designed based on the
traffic data obtained from traffic control stations in northern California.
Extensive experiments conducted on the designed benchmark reflect a successful
congestion prediction
Diagnosing Human-object Interaction Detectors
Although we have witnessed significant progress in human-object interaction
(HOI) detection with increasingly high mAP (mean Average Precision), a single
mAP score is too concise to obtain an informative summary of a model's
performance and to understand why one approach is better than another. In this
paper, we introduce a diagnosis toolbox for analyzing the error sources of the
existing HOI detection models. We first conduct holistic investigations in the
pipeline of HOI detection, consisting of human-object pair detection and then
interaction classification. We define a set of errors and the oracles to fix
each of them. By measuring the mAP improvement obtained from fixing an error
using its oracle, we can have a detailed analysis of the significance of
different errors. We then delve into the human-object detection and interaction
classification, respectively, and check the model's behavior. For the first
detection task, we investigate both recall and precision, measuring the
coverage of ground-truth human-object pairs as well as the noisiness level in
the detections. For the second classification task, we compute mAP for
interaction classification only, without considering the detection scores. We
also measure the performance of the models in differentiating human-object
pairs with and without actual interactions using the AP (Average Precision)
score. Our toolbox is applicable for different methods across different
datasets and available at https://github.com/neu-vi/Diag-HOI
Online Action Detection
In online action detection, the goal is to detect the start of an action in a
video stream as soon as it happens. For instance, if a child is chasing a ball,
an autonomous car should recognize what is going on and respond immediately.
This is a very challenging problem for four reasons. First, only partial
actions are observed. Second, there is a large variability in negative data.
Third, the start of the action is unknown, so it is unclear over what time
window the information should be integrated. Finally, in real world data, large
within-class variability exists. This problem has been addressed before, but
only to some extent. Our contributions to online action detection are
threefold. First, we introduce a realistic dataset composed of 27 episodes from
6 popular TV series. The dataset spans over 16 hours of footage annotated with
30 action classes, totaling 6,231 action instances. Second, we analyze and
compare various baseline methods, showing this is a challenging problem for
which none of the methods provides a good solution. Third, we analyze the
change in performance when there is a variation in viewpoint, occlusion,
truncation, etc. We introduce an evaluation protocol for fair comparison. The
dataset, the baselines and the models will all be made publicly available to
encourage (much needed) further research on online action detection on
realistic data.Comment: Project page:
http://homes.esat.kuleuven.be/~rdegeest/OnlineActionDetection.htm
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